Degradation of polycyclic aromatic hydrocarbons (PAHs) in contaminated soils by Fenton's reagent: a multivariate evaluation of the importance of soil characteristics and PAH properties

In this study, we investigated how the chemical degradability of polycyclic aromatic hydrocarbons (PAHs) in aged soil samples from various contaminated sites is influenced by soil characteristics and by PAH physico-chemical properties. The results were evaluated using the multivariate statistical tool, partial least squares projections to latent structures (PLS). The PAH-contaminated soil samples were characterised (by pH, conductivity, organic matter content, oxide content, particle size, specific surface area, and the time elapsed since the contamination events, i.e. age), and subjected to relatively mild, slurry-phase Fenton's reaction conditions. In general, low molecular weight PAHs were degraded to a greater extent than large, highly hydrophobic variants. Anthracene, benzo(a)pyrene, and pyrene were more susceptible to degradation than other, structurally similar, PAHs; an effect attributed to the known susceptibility of these compounds to reactions with hydroxyl radicals. The presence of organic matter and the specific surface area of the soil were clearly negatively correlated with the degradation of bi- and tri-cyclic PAHs, whereas the amount of degraded organic matter correlated positively with the degradation of PAHs with five or six fused rings. This was explained by enhanced availability of the larger PAHs, which were released from the organic matter as it degraded. Our study shows that sorption of PAHs is influenced by a combination of soil characteristics and physico-chemical properties of individual PAHs. Multivariate statistical tools have great potential for assessing the relative importance of these parameters.     

The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon,granule and tablet properties using multivariate data analysis

Objective: While previous research has demonstrated roller compaction operating parameters strongly influence the properties of the final product, a greater emphasis might be placed on the raw material attributes of the formulation. There were two main objectives to this study. First, to assess the effects of different process variables on the properties of the obtained ribbons and downstream granules produced from the rolled compacted ribbons. Second, was to establish if models obtained with formulations of one active pharmaceutical ingredient (API) could predict the properties of similar formulations in terms of the excipients used, but with a different API.

Materials and methods: Tolmetin and acetaminophen, chosen for their different compaction properties, were roller compacted on Fitzpatrick roller compactor using the same formulation. Models created using tolmetin and tested using acetaminophen. The physical properties of the blends, ribbon, granule and tablet were characterized. Multivariate analysis using partial least squares was used to analyze all data.

Results: Multivariate models showed that the operating parameters and raw material attributes were essential in the prediction of ribbon porosity and post-milled particle size. The post compacted ribbon and granule attributes also significantly contributed to the prediction of the tablet tensile strength.

Conclusions: Models derived using tolmetin could reasonably predict the ribbon porosity of a second API. After further processing, the post-milled ribbon and granules properties, rather than the physical attributes of the formulation were needed to predict downstream tablet properties. An understanding of the percolation threshold of the formulation significantly improved the predictive ability of the models.     

High shear wet granulation: Improved understanding of the effects of process variables on granule and tablet properties of a high-dose,high-hydrophobicity API based on quality by design and multivariate analysis approaches

High shear wet granulation (HSWG), as a widely used granulation technology, has been studied extensively. However, for the HSWG of formulations containing hydrophobic components, the influence of process variables on the properties of granules and tablets has not been reported. In the present study, based on a combination of quality by design and multivariate analysis (MVA) approaches, quercetin with high-dose and high-hydrophobicity was used to study the relationship between process variables, granule properties, and tablet properties in HSWG systematically. Control and response variables were determined using risk assessment. The optimal fitting empirical models established by Box-Behnken design showed that the liquid to solid ratio and impeller speed were the most important factors, which affected all product properties except Carr’s index and yield pressure. Instead, the influence of wet massing time was relatively small (only the effects on yield, granule size, granule hardness, and compression ratio were significant). Then, the process design space was obtained by limiting the related critical quality attributes, which was verified effectively. Scanning electron microscope images showed that smooth granules were produced using higher process parameters, whereas rough and porous granules resulted at lower process parameters. Furthermore, the MVA results demonstrated that increasing the granule hardness led to an increase in the compression ratio and a decrease in tensile strength of the tablets. Tablet fragility and disintegration time were mainly affected by granule density and bulk density, respectively, and both were negatively correlated. The established research paradigm is not only conducive to the successful development of quercetin products, but also provides valuable guidance for improving HSWG–based product development with such formulation characteristics.     

A study on the validity of a task complexity measure for emergency operating procedures of nuclear power plants—Comparing task complexity scores with two sets of operator response time data obtained under a simulated SGTR

The appropriateness of the task complexity (TACOM) measure that can quantify the complexity of procedural tasks was validated in this study. To this end, two sets of task performance time data that have been extracted under the simulated steam generator tube rupture (SGTR) conditions of the reference nuclear power plant (NPP) A and B were compared with the associated TACOM scores. As a result, it was observed that two sets of task performance time data seem to be soundly explained by the associated TACOM scores. Although more additional activities should be conducted to clarify the appropriateness of the TACOM measure, the result of this study provides a crucial clue supporting that the complexity of emergency tasks stipulated in emergency operating procedures (EOPs) can be properly quantified by the TACOM measure.     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

An integrated,finite element‐based process model for the analysis of flow,heat transfer,and solidification in a continuous slab caster

An integrated, finite element‐based process model is presented for the prediction of full three‐dimensional flow, heat transfer, and solidification occurring in a continuous caster. Described in detail are the basic models for the analysis of turbulent flow and heat transfer in the liquid steel zone, in the zone of mixture of the liquid steel and solidified steel, and in the solidified zone. Then, the models are integrated to form a process model which can take into account the strong interdependence between the heat transfer behaviour and the flow behaviour. The capability of the process model to reveal the detailed aspects of turbulent flow, heat transfer, and solidification occurring in a continuous caster is demonstrated through a series of process simulations. Copyright © 2003 John Wiley & Sons, Ltd.